DeeperCut: A Deeper, Stronger, and Faster Multi-Person Pose Estimation Model
This work addresses pose estimation in scenes with multiple people, which is incremental but offers strong gains.
The paper tackled multi-person pose estimation by proposing improved body part detectors, novel image-conditioned pairwise terms, and an incremental optimization strategy, resulting in significantly outperforming best known multi-person pose estimation results with competitive single-person performance.
The goal of this paper is to advance the state-of-the-art of articulated pose estimation in scenes with multiple people. To that end we contribute on three fronts. We propose (1) improved body part detectors that generate effective bottom-up proposals for body parts; (2) novel image-conditioned pairwise terms that allow to assemble the proposals into a variable number of consistent body part configurations; and (3) an incremental optimization strategy that explores the search space more efficiently thus leading both to better performance and significant speed-up factors. Evaluation is done on two single-person and two multi-person pose estimation benchmarks. The proposed approach significantly outperforms best known multi-person pose estimation results while demonstrating competitive performance on the task of single person pose estimation. Models and code available at http://pose.mpi-inf.mpg.de